We conduct experiments in training the rotational Particle Convolution Network for two benchmark problems in jet physics: quark/gluon identification, and top tagging.
The details of these datasets are described in Sec. REF .
For both tasks, we train a general PCN as well as the IRC-safe variant.
The coordinates \(\vec{x}\)  are centered about the jet axis in the \(\eta - \phi \)  plane, as described in [1]}, and the scalar feature for each particle is the particle's transverse momentum, i.e. \(q_k = p_{\mathrm {T},k}\) , which is invariant w.r.t. the coordinate-centering operation.
In the case of the q/g tagging task, we also test the performance of models when supplied with per-particle identification information as well, \(q_k = (p_{\mathrm {T},k}, \mathrm {PID}_k)\) .
